Abstract
The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.
Highlights
The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation
For the sake of brevity, and because we are most interested in modeling Census, we only show the portion of the fitted vector error correction model (VECM) pertaining to Census
We obtained the results shown in Table 3: An overall omnibus test for the Census component of the VECM was statistically significant (F0 = 3.393 on 5 and 101 degrees of freedom; p-value = 0.0071)
Summary
The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. This has the capacity to overwhelm regional health care resources (e.g., Northern Italy and New York) Acknowledging this risk, health care systems have implemented crisis planning to guide infection management, bed capacity, and secure vital supplies (e.g., ventilators and personal protective equipment)[10,11,12]. Google Trends is one of the most popular tools that allows researchers to pull search query data of a random, representative sample drawn from billions of daily searches on Google-associated search e ngines[18]
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